Printed natural-language documents continue to represent a widely used communications medium among individuals, within organizations, and for distribution of information among information consumers. With the advent of ubiquitous and powerful computational resources, including personal computational resources embodied in smart phones, pads, tablets, laptops, and personal computers, as well as larger-scale computational resources embodied in cloud-computing facilities, data centers, and higher-end servers within various types of organizations and commercial entities, natural-language information is, with increasing frequency, encoded and exchanged in electronic documents.
Printed documents are essentially images, while electronic documents contain sequences of numerical encodings of natural-language symbols and characters. Because electronic documents provide advantages in cost, transmission and distribution efficiencies, ease of editing and modification, and robust-storage over printed documents, an entire industry supporting methods and systems for transforming printed documents into electronic documents has developed over the past 50 years.
Computational optical-character-recognition methods and systems and electronic scanners together provide reliable and cost-effective imaging of printed documents and computational processing of the resulting digital images of text-containing documents to generate electronic documents corresponding to the printed documents.
With the advent of camera-containing smart phones and other mobile, processor-controlled imaging devices, digital images of text-containing documents can be generated by a large variety of different types of ubiquitous, hand-held devices, including smart phones, inexpensive digital cameras, inexpensive video surveillance cameras, and imaging devices included in mobile computational appliances, including tablets and laptops. Furthermore, some of these devices have memories that store a plethora of images (including text images and the like) that a user may be desirous of having Optical Character Recognition (OCR) processed.
Digital images of text-containing documents produced (or stored) by these hand-held devices and appliances can then be processed, by computational optical-character-recognition systems, including optical-character-recognition applications in smart phones, to produce corresponding electronic documents. Typically, the digital image captured by the user electronic device is transmitted, via a communication network, to a server of the optical-character-recognition systems for performing the server-based (OCR) function (as opposed to a locally-executed OCR function, which tends to result in a lower quality of the output of the OCR).
In order to ensure an acceptable speed of transmission of the digital image to the server, as well as to save on used bandwidth associated with the transmission via the communication network, it is known to compress the digital image into a compressed digital image to transmit to the server. The compression is done using a codec that uses a compression algorithm, such as Joint Photographic Experts Group (JPEG) or JPEG 2000. A typical compression algorithm is associated with a compression parameter, such as a compression coefficient.
The server then (i) receives the compressed digital image, (ii) de-compresses the compressed digital image to obtain a de-compressed digital image, and (iii) executes the server-based OCR function to generate a recognized text document based on the de-compressed digital image, the recognized text document based containing text generated on the basis of the de-compressed digital image. The server can then transmit back the recognized text document to the user electronic device via the communication network (in an original or a compressed state thereof).
One of the reasons for implementing optical-character-recognition system with the server-based OCR function (as opposed to the locally-executed OCR function) is the quality of the digital image produced by such hand-held document imaging (for example, the digital images produced by hand held devices tend to be associated with increased noise, optical blur, and other defects and deficiencies in the text-containing digital images produced by the hand-held devices and appliances in comparison with dedicated document-scanning appliances, for example).
The server-based OCR function involves pre-processing of the de-compressed digital image in order to reduce the number of artefacts in the de-compressed digital image (i.e. reducing noise, reducing optical blur, etc.). The server also, as part of the server-based OCR function, executes a high quality binarization and computational-intensive OCR routines. Some or all of these may be impractical to implement locally on the user electronic device (for example, the computational power required for such processes may not be available on the user electronic devices).